Apple Inc.

Other

Amritpal Singh Saini

Ian M Richter

Olivier Soares

Michael J Rockwell

Siva Chandra Mouli Sivapurapu

Jian Zhang

Eshan Verma

Adrian Peter Lindberg

Aashi Manglik

Edward S Ahn

Pedro Manuel Da Silva Quelhas

In one implementation, a method includes presenting, via a display device, a first synthesized reality (SR) view of an event that includes SR content associated with the event. The SR content includes a plurality of related layers of SR content that perform actions associated with the event. The method includes detecting, via one or more input devices, selection of a respective layer among the plurality of related layers of SR content associated with the event. The method includes presenting, via the display device, a second SR view of the event that includes the respective layer of SR content in response to the selection of the respective layer. The second SR view corresponds to a point-of-view of the respective layer.

Method and devices for presenting and manipulating conditionally dependent synthesized reality content threads

Various implementations disclosed herein include devices, systems, and methods for presenting objective-effectuators in synthesized reality settings. In various implementations, a device includes a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, a method includes instantiating an objective-effectuator into a synthesized reality setting. In some implementations, the objective-effectuator is characterized by a set of predefined actions and a set of visual rendering attributes. In some implementations, the method includes obtaining an objective for the objective-effectuator. In some implementations, the method includes determining contextual information characterizing the synthesized reality setting. In some implementations, the method includes generating a sequence of actions from the set of predefined actions based on the contextual information and the objective. In some implementations, the method includes modifying the objective-effectuator based on the sequence of actions.

Objective-effectuators in synthesized reality settings

In one implementation, a method includes: instantiating a first objective-effectuator (OE) associated with first attributes and a second OE associated with second attributes into a synthesized reality (SR) setting, wherein the first OE is encapsulated within the second OE; providing a first objective to the first OE based on the first and second attributes; providing a second objective to the second OE based on the second attributes, wherein the first and second objectives are associated with a time period between a first and second temporal points; generating a first set of actions for the first OE based on the first objective and a second set of actions for the second OE based on the second objective; and rendering for display the SR setting for the time period including the first set of actions performed by the first OE and the second set of actions performed by the second OE.

A method includes obtaining, by a motion generator that has been trained to generate torque values for a plurality of joints of a rig associated with a target, a set of parameters associated with a target motion. The method includes, in response to the target motion being a first type of motion, generating a first set of torque values for the plurality of joints based on the set of parameters and a set of previous poses of the target. The method includes, in response to the target motion being a second type of motion, generating a second set of torque values for the plurality of joints based on the set of parameters and the set of previous poses of the target. The method includes triggering a movement of the target in accordance with the first set of torque values or the second set of torque values.

Multi-motion generator

In some implementations, a method includes: obtaining a logical representation of a fixed function node; generating, by concerted operation of the logical representation of the fixed function node and a machine learning node that interfaces with the logical representation of the fixed function node, a candidate result based on a set of image data frames; determining whether error criteria are satisfied based at least in part on a comparison between the candidate result and a predetermined result for the set of image data frames; and, in response to determining that the error criteria are satisfied, modifying at least one of: a first portion of operating parameters of the machine learning node associated with operations of the machine learning node; and a second portion of operating parameters of the machine learning node associated with interfacing operations between the machine learning node and the logical representation of the fixed function node.

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San Jose, CA, United States of America

Amritpal Singh Saini